texmex-internal: Internal functions for texmex

Description

Internal functions used by the texmex package.

Usage

## S3 method for class 'gpd':
hist(x, xlab, ylab, main, ...)
qqgpd(object, nsim = 1000, alpha = 0.05, xlab, ylab, main, plot = TRUE,
     ylim = "auto", pch=1, col = 2, cex = 0.75, linecol = 4, intcol = 0,
     polycol = 15)
ppgpd(object, nsim = 1000, alpha = 0.05, xlab, ylab, main,
     pch=1, col = 2, cex = 0.75, linecol = 4, intcol = 0,
     polycol = 15)
qgpd2(N, sigma = 1, xi = 1, u = 0, la = 1)
u2gpd(u, p=1, th=0, sigma, xi)
mexTransform(x, method = "mixture", divisor = "n+1", na.rm=TRUE, 
     margins="laplace")
fittedGPDscale(o)
fittedGPDshape(o)
revTransform(x, data, qu, th=0, sigma=1, xi=0, method="mixture")
gpdFit(y, th, X.phi, X.xi, penalty="none", start=NULL,
        priorParameters = NULL, maxit = 10000, trace = 0, hessian = TRUE)
info.gpd(o, method="observed")
ConstraintsAreSatisfied(a,b,z,zpos,zneg,v)
PosGumb.Laplace.negloglik(yex, ydep, a, b, m, s, constrain, v, aLow)
PosGumb.Laplace.negProfileLogLik(yex, ydep, a, b, constrain, v, aLow)
Profile_likelihood_HT_unc(par,listr,x,silly=-10^(40))
estimate_HT(list,u,pars,params=TRUE)
Dcond(x,a,b,c,d,zi,zk)
roots(lev,a,c,b,d,Zj,Zk) 
Profile_likelihood_cd_nm_joint_D_KT_neg(par,listr,x,Zestfun,...,v,silly=-10^(40))
Profile_likelihood_cd_nm_joint_D_KT(par,listr,x,Zestfun,...,v,silly=-10^(40))
profile_minmax_joint_posneg_KT (pars,listdata,u,q1=0,q2=1,...,sill=-10^(40))
initial_posneg(D,...)
estimate_HT_KPT_joint_posneg_nm(pars,x,listr,params=TRUE,...,k=3)
inv_Laplace(p)
gpdDelta(A, K)
addCov(res,X)
namesBoot2bgpd(bootobject)
plotRLgpd(M,xm,polycol,cicol,linecol,ptcol,n,xdat,pch,smooth,xlab,ylab,
  main,xrange,yrange)
rFrechet(n)
rMaxAR(n,theta)
.extRemes.decluster.intervals(Z, ei) 
.extRemes.decluster.runs(Z, r) 
.extRemes.exi.intervals(Z)

Arguments

x, object, data

Object to be used by plot functions, vector to be converted.

xlab, ylab, ylim, main, pch, col, cex, linecol, intcol, polycol, ...

Arguments to plot functions.

Number of observations corresponding to N-observation return level to be calculated.

Rate at which threshold is exceeded.

alpha

Control nominal coverage of condfidence intervals. Defaults to alpha = 0.05.

nsim

Number of simulated datasets to use in computing confidence intervals.

plot

Whether or not to plot the result.

Uniform deviates to be converted to GPD deviates.

p, th, qu, sigma, xi

Paramters of GPD distribution.

method

Argument to mexTransform: how to convert. When method = 'mixture', the upper tail of the distribution is modelled using a generalized Pareto distribution and the remainder is approximated using the empirical distribution.

divisor

Divisor used in estimation of empirical distribution.

na.rm

Whether or not to remove missing values.

margins

Form of margins to which to transform x. Can take values margins="laplace" or margins="gumbel".

y, X.phi, X.xi, penalty, start, priorParameters, maxit, trace

Arguments supplied to gpd, migpd or mex, or inferred from those functions after some preprocessing.

hessian

Argument passed to optim. Logical.

An object of class 'gpd'.

a,b

Dependence parameters of the Heffernan and Tawn dependence model.

m,s

Nuisance parameters of the Heffernan and Tawn dependence model.

z,zpos,zneg

Quantiles of the residuals under the fitted Heffernan and Tawn model, asymptotic positive dependence and asymptotic negative dependence respectively.

Positive scalar, tuning parameter for constrained estimation of Heffernan and Tawn dependence model under estimation with Laplace marginal distributions.

constrain

Logical. Whether to carry out estimation of Heffernan and Tawn model parameters under correct stochastic ordering of fitted model and asymptotic positive/negative dependence models.

aLow

Lower bound for dependence parameter a. This depends on the marginal distribution under which the dependnece model is being fittted. Under Gumbel margins, the lower bound is 0 and under Laplace margins, the lower bouind is -1.

yex, ydep

Data for model estimation: yex is the explanatory variable on which the model conditions, and ydep is the dependent variable.

par, listr, silly, list, pars, params, c, d, zi, zk, lev, Zj, Zk, Zestfun, listdata, q1, q2, sill, D, k

Arguments to independently coded test functions implementing constrained estimation of the H+T2004 conditional dependence model with Laplace margins.

A, K

Arguments to gpdDelta which is used internally to find approximate standard errors for return levels for gpd-type objects.

res,X

Arguments to addCov which is used internally to bind covariates on to rows of reported return levels or linear predictors.

bootobject

Argument to namesBoot2bgpd which restructures an object of class bootgpd to resemble one of class bgpd, which can then use methods for the bgpd class.

M,xm,cicol,ptcol,n,xdat,smooth,xrange,yrange

Arguments to plotRLgpd which is a worker function, does the actual plotting for plotrl.gpd, plot.rl.gpd, pl

theta

Argument to rFrechet and rMaxAR, the dependence parameter theta. Takes values between 0 and 1, with 0 corresponding to perfect dependence and 1 to independence.

Z,r,ei

Arguments to extRemes package functions used for testing of declustering code. Numeric Z logical indicating threshold excess, r run length for runs declustering and ei extremal index estimate.

Details

None of these functions are intended to be used explicitly.

The plotting functions are used internally by plot.gpd.

Some of the code is based on code that appears in the ismev package, originally written by Stuart Coles, the evd package by Alec Stephenson and extRemes package by Eric Gilleland, Rick Katz and Greg Young.

Code to carry out estimation of H+T2004 under Laplace margins and constrained estimation was written by Yiannis Papastathopoulos, and is used here for validation purposes.